English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this work, we propose a novel perspective to the problem of patchcorrectness assessment: a correct patch implements changes that "answer" to aproblem posed by buggy behaviour. Concretely, we turn the patch correctnessassessment into a Question Answering problem. To tackle this problem, ourintuition is that natural language processing can provide the necessaryrepresentations and models for assessing the semantic correlation between a bug(question) and a patch (answer). Specifically, we consider as inputs the bugreports as well as the natural language description of the generated patches.Our approach, Quatrain, first considers state of the art commit messagegeneration models to produce the relevant inputs associated to each generatedpatch. Then we leverage a neural network architecture to learn the semanticcorrelation between bug reports and commit messages. Experiments on a largedataset of 9135 patches generated for three bug datasets (Defects4j, Bugs.jarand Bears) show that Quatrain can achieve an AUC of 0.886 on predicting patchcorrectness, and recalling 93% correct patches while filtering out 62%incorrect patches. Our experimental results further demonstrate the influenceof inputs quality on prediction performance. We further perform experiments tohighlight that the model indeed learns the relationship between bug reports andcode change descriptions for the prediction. Finally, we compare against priorwork and discuss the benefits of our approach.

Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness